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All Journal Jurnal EECCIS JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics International Journal of Advanced Science Computing and Engineering
Rita Afyenni
Politeknik Negeri Padang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

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Journal : JOIV : International Journal on Informatics Visualization

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Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model Hamad Khaleefah, Shihab; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Farooq Khattak, Umar; Ahmed Jubair, Mohammed; Afyenni, Rita
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2758

Abstract

A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.
A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment Hassan, Mustafa Hamid; Mostafa, Salama A.; Baharum, Zirawani; Mustapha, Aida; Saringat, Mohd Zainuri; Afyenni, Rita
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1228

Abstract

The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy.
Co-Authors -, Defni A. Mostafa, Salama Ahmad Rayhan Ahmed Jubair, Mohammed Aida Mustapha, Aida Aldo Erianda, Aldo Baharum, Zirawani Farooq Khattak, Umar Firosha, Ardian Gunasekaran, Saraswathy Shamini Gusman, Taufik Hamad Khaleefah, Shihab Hassan, Mustafa Hamid Isyrin Apridal, Fauzi Kasmar, Andre Febrian Kirana, Salsabila Okta Meri Azmi Mostafa, Salama A. Nur Azizah PUTRI WAHYUNI Rahmat Hidayat Rika Idmayanti Saringat, Mohd Zainuri Sukma, Fanni Sumema Yance Sonatha